---
title: RAG with LanceDB and SQLite
---

## Code

```python
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.knowledge.embedder.ollama import OllamaEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.ollama import Ollama
from agno.vectordb.lancedb import LanceDb

# Define the database URL where the vector database will be stored
db_url = "/tmp/lancedb"

# Configure the language model
model = Ollama(id="llama3.1:8b")

# Create Ollama embedder
embedder = OllamaEmbedder(id="nomic-embed-text", dimensions=768)

# Create the vector database
vector_db = LanceDb(
    table_name="recipes",  # Table name in the vector database
    uri=db_url,  # Location to initiate/create the vector database
    embedder=embedder,  # Without using this, it will use OpenAIChat embeddings by default
)

knowledge = Knowledge(
    vector_db=vector_db,
)

knowledge.add_content(
    name="Recipes", url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)



db = SqliteDb(db_file="data.db")


agent = Agent(
    session_id="session_id", 
    user_id="user",  
    model=model,
    knowledge=knowledge,
    db=db,
)


agent.print_response(
    "What is the first step of making Gluai Buat Chi from the knowledge base?",
    markdown=True,
)
```

## Usage

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Install Ollama">
    Follow the installation instructions at [Ollama's website](https://ollama.ai)
  </Step>

  <Step title="Install libraries">
    ```bash
    pip install -U lancedb sqlalchemy agno
    ```
  </Step>

  <Step title="Run Agent">
    <CodeGroup>
    ```bash Mac
    python cookbook/agents/rag/rag_with_lance_db_and_sqlite.py
    ```

    ```bash Windows
    python cookbook/agents/rag/rag_with_lance_db_and_sqlite.py
    ```
    </CodeGroup>
  </Step>
</Steps>